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  • Discrete event simulation modelling to evaluate the impact of a quality improvement initiative on patient flow in a paediatric emergency department

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Original research
Discrete event simulation modelling to evaluate the impact of a quality improvement initiative on patient flow in a paediatric emergency department
  1. Kenneth W McKinley1,
  2. John Babineau2,
  3. Cindy G Roskind2,
  4. Meridith Sonnett2,
  5. Quynh Doan3
  1. 1 Emergency Medicine Section of Data Analytics, Children's National, Washington, DC, USA
  2. 2 Department of Emergency Medicine, New York-Presbyterian Hospital/Columbia University Medical Center, New York, NY, USA
  3. 3 Department of Pediatrics, British Columbia Children's Hospital, Vancouver, BC, Canada
  1. Correspondence to Dr Kenneth W McKinley, Children's National, Washington, DC 20010, USA; kmckinley{at}childrensnational.org

Abstract

Objective We developed a discrete event simulation model to evaluate the impact on system flow of a quality improvement (QI) initiative that included a time-specific protocol to decrease the time to antibiotic delivery for children with cancer and central venous catheters who present to a paediatric ED with fever.

Methods The model was based on prospective observations and retrospective review of ED processes during the maintenance phase of the QI initiative between January 2016 and June 2017 in a large, urban, academic children’s hospital in New York City, USA. We compared waiting time for full evaluation (WT) and length of stay (LOS) between a model with and a model without the protocol. We then gradually increased the proportion of patients receiving the protocol in the model and recorded changes in WT and LOS.

Results We validated model outputs against administrative data from 2016, with no statistically significant differences in average WT or LOS for any emergency severity index (ESI). There were no statistically significant differences in these flow metrics between the model with and the model without the protocol. By increasing the proportion of total patients receiving this protocol, from 0.2% to 1.3%, the WT increased by 2.8 min (95% CI: 0.6 to 5.0) and 7.6 min (95% CI: 2.0 to 13.2) for ESI 2 and ESI 3 patients, respectively. This represents a 14.0% increase in WT for ESI 3 patients.

Conclusions Simulation modelling facilitated the testing of system effects for a time-specific protocol implemented in a large, urban, academic paediatric ED, showing no significant impact on patient flow. The model suggests system resilience, demonstrating no detrimental effect on WT until there is a 7-fold increase in the proportion of patients receiving the protocol.

  • quality improvement
  • emergency department
  • paediatrics, paediatric emergency medicine
  • research, operational

https://doi.org/10.1136/emermed-2019-208667

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    Footnotes

    • Presented at Presented as a platform presentation at the American Academy of Pediatrics National Conference and Exhibition on 2 November 2018 in Orlando, FL.

    • Contributors KWM, JB and MS conceived of the study and QD suggested the specific modelling approach. KWM wrote the protocol for the study and oversaw the conduct of the study with CGR. KWM and QD designed the discrete event simulation model and KWM performed the data analysis. KWM drafted the manuscript with review and revisions by QD, JB, MS and CGR. KWM and QD take responsibility for the paper as a whole.

    • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

    • Competing interests None declared.

    • Patient consent for publication Not required.

    • Ethics approval The institutional review board of Columbia University Medical Center approved protocol IRB-AAAQ7123 for this project.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Data availability statement Data are available upon reasonable request. Complete lists of durations for each process are available on request to the corresponding author. These data do not include dates or other patient-specific information.